Neural network system and methods for analysis of organic materials and structures using spectral data

ABSTRACT

Apparatus and processes for recognizing and identifying materials. Characteristic spectra are obtained for the materials via spectroscopy techniques including nuclear magnetic resonance spectroscopy, infrared absorption analysis, x-ray analysis, mass spectroscopy and gas chromatography. Desired portions of the spectra may be selected and then placed in proper form and format for presentation to a number of input layer neurons in an offline neural network. The network is first trained according to a predetermined training process; it may then be employed to identify particular materials. Such apparatus and processes are particularly useful for recognizing and identifying organic compounds such as complex carbohydrates, whose spectra conventionally require a high level of training and many hours of hard work to identify, and are frequently indistinguishable from one another by human interpretation.

This invention was made with government support under Grant/Contractnumbers DE-FG09-85ER13426 and DE-FG09-87ER13810 awarded by theDepartment of Energy. The government has certain rights in theinvention.

This invention relates to the use of neural networks to analyze andidentify particular materials by recognizing patterns in spectra thatare characteristic of such materials. The invention has particularutility in analyzing and identifying complex organic molecules, such as,for instance, complex carbohydrate.

BACKGROUND OF THE INVENTION

In general, the incorporation of digital systems into modern analyticalinstrumentation has generated immense quantities of data. The increasesin the quantity of data collected have not been matched by correspondinggains in information extraction techniques. An important step in moreefficient and effective information extraction is the development ofpattern recognition systems capable of handling data that are generatedby different analytical techniques.

Researchers commonly use mass, infrared and nuclear magnetic resonanceand other spectra in solving structure elucidation problems of variousmaterials, and particularly organic molecules. The amount of informationproduced by these techniques can be overwhelming. The need to extractinformation from such large databases has given rise to the developmentof computerized information systems. The abilities of these informationsystems vary from retrieval of stored spectra to pattern recognition tospectral simulation. The systems that have been developed are based onlibrary search and interpretative techniques.

PREVIOUS TECHNIQUES

The computer-based information systems that use library search methodscompare unknown spectra to each spectrum in a reference library. Eachspectrum is typically stored in a reduced form to expedite the retrievaland comparison process. Systems using library search methods are themost common type of computer-based information systems available. Someof the earliest systems were created for libraries of mass spectra, andmass spectral search systems continue to be developed. Library searchsystems have also been reported for infrared spectra, and many ¹³ C-NMRdatabases and associated retrieval techniques have been developed.Efforts are underway to create a database for ¹³ C-NMR spectraoriginating from biological sources.

The power of ¹ H-NMR techniques in determining carbohydrate structureshas been demonstrated repeatedly. Databases containing ¹ H-NMR spectrahave been developed, and pattern recognition techniques can be appliedto 2-D NMR spectra. However, development of computer-aided librarysearch methods for ¹ H-NMR spectra is complicated by the relatively poorreproducability of the spectra of a given molecule under normalexperimental conditions. A retrieval method for ¹ H-NMR spectra based onchemical shifts for spectra acquired under highly controlled conditionshas been developed, for instance, but presently requires standardizedconditions.

Systems based on an interpretative approach to structure elucidation usedata-structure representations that differ from library search methods.Database systems developed to assist researchers in the interpretationof analytical results contain spectral data as well as information suchas how the sample was prepared, its origin, its concentration, etc. Thisprocedural information is required for the available methods of advancedinterpretation of particular spectra for structural identification.Systems that use interpretative methods for handling chemicalinformation have played a pioneering role in the evolution of softwareused in expert system development. Examples are DENDRAL [see R. K.Lindsay, et al., Applications of Artificial Intelligence for OrganicChemistry: the DENDRAL Project (1980); D. H. Smith, et al., 133 Anal.Chim. Acta 471 (1981)]; DARC [see J. E. Dubios, et al., 25 J. Chem. Inf.Comput. Sci 326-33 (1985)]; CASE see C. A. Shelley, et al., 133 Anal.Chim. Acta 507-16 (1981); C. A. Shelley, et al., "Computer AssistedStructure Elucidation," 54 ACS Symposium Series, p. 92 (1977)] andCHEMICS [see H. I. Abe, et al., 1 Comput. Enhanced Spectrosc. 55-62(1983); S. Sasaki, et al., Computer Applications In Chemistry, 185-206(S. Heller, et al., ed. 1983)].

In the oligo- and polysaccharide field, for instance, the ¹ H-NMRsignals of a glycosyl residue carry information on the nature of thatresidue and on the environment of the residue within the molecule. Theinfluence of the molecular environment includes the points of attachmentof other glycosyl residues and non-glycosyl substituents to the residuein question. Furthermore, the orientation in space of the residues inquestion relative to neighboring residues affects the chemical shifts ofNMR signals of the residue in question. This has been experimentallyproven by showing that the ¹³ C-NMR chemical shifts of oligosaccharidesdepend on the conformation of the glycosidic bonds. This concept hasbeen used to analyze glycosidation shifts of ¹³ C-NMR spectra ofoligosaccharides which, in turn led to the development of the programCASPER.

The success of the structural reporter group concept established asimilar dependence of ¹ H-NMR chemical shifts on the residues close inspace. However, the structural reporter group concept has limitationsbecause it uses only a few of the NMR signals to identify glycosylresidues of oligosaccharides. The structural reporter group conceptfails to work in many circumstances because the chemical shift for theanomeric proton of a glycosyl residue is affected by changes in theproton's chemical environment. Even though these analytical tools arehelpful in interpreting NMR spectra, analysis of all of the NMR signalsfrom oligosaccharides is a far more reliable way to fully characterizetheir structures. However, it requires great skill, relatively largeamounts of highly purified samples and costly instrument time foranalysts to completely assign all the signals of NMR spectra byavailable 1-D and 2-D techniques.

NEURAL NETWORKS

The ability of artificial neural networks to recognize patterns hasrecently received much attention. The underlying theme behind thedevelopment of artificial neural networks was an attempt to simulate theparallel processing of the human brain deduced from the perceived mannerby which the brain recognizes pictures or speech. A variety of neuralnetwork architectures and training schemes have been described andvariations in the response behavior of neural networks have beenreported.

A common type of neural network known as a hidden-layer feedforwardnetwork consists of an input layer of neurons or nodes, at least onehidden layer, and an output layer. The neuron layers are linked via aset of synaptic interconnections that are defined at the design stage ofthe network. Each neuron in the input layer is typically connected toeach neuron in the hidden layer, and each neuron in the hidden layer istypically connected to each neuron in the output layer, via a synapticconnection; these may be physical, electronic connections, or they maybe embodied in software, as may be the neurons themselves, whichsoftware operates on conventional digital computers. The network istrained by presenting the desired response to the output layer ofneurons and by simultaneously presenting the input neuron layer with thepatterns that need to be distinguished. Connection strengths aredeveloped by the network as it uses one of several learning algorithms.After a certain number of training iterations, information may bepresented to the input neurons, which then propagate signals through thenetwork in a feedforward (afferent) manner ultimately causing the outputlayer to indicate a proper response.

Neural networks having no hidden layers, sometimes referred to as"perceptrons," may also be used in the present invention. Such networksgenerally produce less reliable information than do networks with hiddenlayers when used in applications such as in the present invention,however, perhaps because hidden layers allow a network to map outputpatterns to structurally dissimilar input patterns.

The iterative training process of artificial neural networks extractscharacteristic information from an input in order to decide which outputwill result. Thus, in contrast to a rule-based system in which theexpert must specify the constraints, neural networks select the rules bythemselves during the training process ("learning"). Each neuron has oneor more input values, one output value, and a threshold. In the inputlayer of neural networks according to the present invention, the outputof a neuron is preferably, but need not be, equal to its input. Theoutput value of any higher level neuron is computed according to anactivation or squashing function using the input values and itsthreshold. The threshold determines "how high" the input to that neuronmust be in order to generate a positive output of that neuron. Theconnection between two neurons is realized in mathematical terms bymultiplying the output of the lower level neuron by the strength of thatconnection (weight). The output response of any hidden layer neuron(o_(j)) and any output layer neuron is a function of the network inputto that neuron defined by the difference of that neuron's threshold (θ)and the input to it. The value of the input into each hidden or outputlayer neuron is weighted with the weight currently stored for theconnection strengths between each of the input and hidden layer neurons,and the hidden and output layer neurons, respectively. Summation overall connections into a particular neuron and subtracting this sum fromthe threshold value may be performed according to the followingsigmoid-type Fermi function:

    o.sub.j =[1+exp (θ.sub.j -Σ.sub.i w.sub.ji * o.sub.i)].sup.-1 ;

where i and j represent neurons of two different layers with jrepresenting the higher layer; θ_(j) represents the bias value for jlayer neuron; w_(ji) represents the strength of the connection betweenneuron i and neuron j. Alternatively, sine-type functions may be used toobtain the desired type of response function for the output of a neuron.A neuron may be considered to be "turned on", for instance, whenever itsvalue is above a predetermined value such as, for instance, 0.9 and"turned off" with a value of less than another value such as 0.1, andhas an undefined "maybe" state between those values. The desired outputpattern for each input pattern is defined by the user. The network,through an iterative back-propagation, establishes a set of weights andthresholds for every neural connection that produces the desired outputpattern for the presented input information. The learned information ofa neural network is contained in the values of the set of weights andthresholds.

The back-propagation learning process is described in D. E. Rumelhart,et al., Parallel Distributed Processing, ch. 8, pp. 322-28 (MIT Press,1986), which is incorporated herein by this reference, and whichrepresents a portion of the state of the art. The procedure involves aset of pairs of input and output vectors. The network uses an inputvector to generate its own, or actual, output vector. The actual outputvector is compared with a desired output, or target, vector. Thesynaptic weights are changed to reduce the difference between the targetvector and the actual output vector. The conventional delta rule is usedfor this calculation; the weight for a particular synapse or connectionbetween units is adjusted proportionally to the product of an errorsignal, delta, available to the unit receiving input via the connectionand the output of the unit sending a signal via the connection. If aunit is an output unit, the error signal is proportional to thedifference between the actual and target value of the unit; if a hiddenlayer, it is determined recursively in terms of the error signals of theunits to which it directly connects and the weights of thoseconnections.

In the back propagation learning process, the input vector is presentedand propagated forward through the network to generate the actual outputvector. That vector is compared with the target vector, resulting in anerror signal for each output unit. Weight changes are then computed forall connections that feed into the output layer. Deltas are thencalculated for all units in the next layer, and the process is repeated.

Other artificial neural network schemes (ANS) include nonlinear networksas described in the works of Stephen Grossberg, including, for instance,S. Grossberg, Neural Networks, (1987). These allow unsupervised learningand perhaps more closely simulate cognitive processes of the human brainthan the back-propagation off-line networks described above. Suchprocedures typically use bidirectional feedback between mathematicalmodels of short- and long-term memory to determine the connectionstrengths between the neurons, and thus allow self-stabilizing adaptivepattern recognition in response to complex real time nonstationary inputenvironments, in distinction to the back propagation, off-linetechniques described above. ("Off-line" as used in this document meansany learning scheme or neural network which does not compensate forshort- and long-term memory in determining the connection strengthsbetween neurons or units.) See, S. Grossberg, Nonlinear Neural Networks:Principles, Mechanisms, and Architectures, lecture at National ScienceFoundation meeting on Neural Networks and Neuromorphic Systems, Woburn,Mass. (Oct. 7, 1986). Such networks may serve as an alternativearchitecture for use of artificial neural networks for the recognitionof materials via their spectra, but there remains a question as towhether the added complexity is justified in an input environment whichis arguably not real time. Furthermore, supervised learning allows thenetwork to incorporate and reflect all previously known materials andtheir corresponding spectra, unlike the Grossberg-type unsupervisedsystems.

In addition to the back-propagation method described above, severalother off-line variations of the learning scheme have been proposed toimprove the speed and stability of the training process. These includestochastic learning, which is said to have superior performance over thesteepest-descent algorithm normally used, and "forgetting" during thelearning process to improve the network's ability to find the globalminimum for the weights and thresholds. Recently, feed-forward neuralnetworks with one hidden layer of neurons have been shown to beeffective in speech recognition; the same architecture shows promise inpredicting, from amino acid sequences, the secondary structure ofproteins. Only a small amount of experimental work has been publisheddemonstrating the utility of neural networks in natural productchemistry. Several attempts have been made to utilize neural networks toresolve 3-D structural patterns of proteins from their amino acidsequences. Networks have been designed that can predict with up to 79%accuracy the secondary structure of peptides from knowledge of theiramino acid sequences. L. H. Holley & M. Karplus, 86 Proc. Acad. Natl.Acad. Sci. USA 152 (1989). The information used to teach the network wasthe available 3-D structures and associated amino acid sequences ofproteins obtained by X-ray crystal structure analyses. Neural networkshave also been successfully used in locating promotor sites in DNAsequences, as discussed in A. V. Lukashin, et al., 6 J. Biomol. Struct.& Dynam. 1123-33 (1989).

SUMMARY OF THE INVENTION

Techniques and apparatus according to the present invention capitalizeon the ability of a neural network to "learn" (store as synaptic weightsand neural threshold values) spectral information relating to a largenumber of materials. The network is used in combination with spectrumanalysis devices, the spectral output of which is analyzed incrementallyin a manner that allows incremental spectral data to be presented to theinput neurons of the network. The network, whose input and outputneurons are previously "trained" with a number of known spectra andidentification data, respectively, identifies, via its output layer, thematerial associated with the particular spectral data presented to theinput layer. The neural network can identify in fractions of a secondmaterials whose identification previously required the expertise andprolonged efforts of graduate level researchers.

The primary advantage of neural networks over standard library searchalgorithms is that the neural network does not require rules definingthe experimental variations which may occur. The neural network approachis potentially more powerful than library searches because differentmolecules show different sensitivities towards the variation ofexperimental conditions. Accommodation for these variations could easilybe implemented into a neural network approach from the training set butis much more difficult to implement into a normal library search.

The present invention is particularly useful in the recognition ofcomplex organic structures. Knowledge of the structures of complexcarbohydrates, for instance, is important in biology and medicine andhas become an important topic in the recombinant protein pharmaceuticalfield. The pattern-recognition capabilities of the present inventionaccelerate the pace of carbohydrate structure analysis by reducing therequired labor and, in many cases, reducing the amount of samplerequired.

Highly trained personnel and sophisticated equipment are required todetermine the primary structure of an oligosaccharide, and, even underthese conditions, the analysis can take many weeks or months toaccomplish. Scientists often find that the structures of thecarbohydrates they are evaluating turn out to be the same or similar tothe structures of molecules that have already been structurallycharacterized which results in a great deal of time and effort beingwasted. Therefore, an object of the invention is to make the structuralinformation of NMR spectra of previously characterized molecules readilyavailable to research scientists. As an example, entry of ˜2000carbohydrate structures taken from the literature into a databasedeveloped in the Complex Carbohydrate Research Center and Department ofBiochemistry at the University of Georgia in Athens, Ga. revealed afteranalysis that only ˜1500 structures were unique. In other words, ˜25% ofthe structures thought to be unique in the literature were duplicates.Other efforts to elucidate carbohydrate structure undoubtedly led tostructures that were recognized as duplicates and were, therefore, notadded to the literature, but the wasted effort by the analyst andexpense were still incurred. Such duplication of effort occurs becausethere is currently no way to determine if the structure of a complexcarbohydrate being characterized has already been described in theliterature until the structure of the carbohydrate under investigationhas been fully elucidated. The neural network-driven pattern recognitionapparatus and techniques according to the present invention will allowresearchers to determine, at a much earlier stage during analysis, ifthe structure they are working on has been characterized previously.

The elucidation of complex carbohydrate structures often relies heavilyon ¹ H-NMR spectra, as they provide a great deal of structuralinformation from a relatively moderate amount (˜100 nmol) of sample. Bycontrast, ¹³ C-NMR spectroscopy requires approximately 100 times more ofthe compound. Other analytical techniques such as mass spectroscopicanalysis and GC and LC retention times are also important in elucidatingthe structure of oligosaccharides, and usually require even less sample(approximately 10 nmol) than is required for ¹ H-NMR analysis. Anotherdifference between ¹³ C-NMR and ¹ H-NMR spectra lies in thereproducability of the spectra. ¹³ C-NMR spectra are much morereproducible than ¹ H-NMR spectra because the carbon atoms are not asstrongly affected as hydrogen atoms by changes in the environment. Thus,normal library search methods are much less suited for ¹ H-NMR spectrathan for ¹³ C-NMR spectra.

The present invention has a number of implications involving theanalysis of biologically important polymers. So far among biopolymers,the structural determination of oligo- and polysaccharides requires themost effort. There is no automatic or semiautomatic procedure for doingthis analysis. The implementation of neural network analyses of 1-D ¹H-NMR spectra in combination with chromatographic data could lead to thedevelopment of a fully automated system for oligosaccharide analysis.This could have major impacts on the diagnosis of glycosyl storagediseases and other enzymatic defects that cause a wrong glycosylation ofproteins or lipids. Furthermore, extension of such techniques may proveto be important for other biopolymers such as proteins, RNAs and DNAs.Aspects of the invention related to the recognition of mass spectra ofpartially methylated alditol acetates could also be extended to otherGC-MS methods.

Artificial neural network-based pattern recognition systems according tothe present invention have been used to identify one-dimensional (1-D) ¹H-NMR spectra of complex carbohydrates Such networks for recognition of¹ H-NMR spectra can compare the spectral pattern of a newly recordedcompound with spectral patterns stored as synaptic weights and neuralthreshold values in the neural network. For instance, software-emulatedartificial neural networks can recognize individual 1-D ¹ H-NMR spectraof large oligosaccharides within a set of closely related 1-D ¹ H-NMRspectra. Neural networks can also accommodate the normal imprecisions of¹ H-NMR spectra, including those resulting from differences in chemicalshifts due to concentration or temperature variations, differentsignal-to-noise (S/N) ratios, variable absolute signal intensities, anddifferent line widths. The ability to accommodate these variables iscritical for a pattern-recognition technique to be useful for structuralanalysis under normal laboratory conditions.

Neural networks may be used according to the present invention todiscriminate between closely related carbohydrates within a largedataset by using free induction decay (FID) data. A primary advantage ofFIDs is that they can provide a wide range of scaling possibilities.Furthermore, neural networks may be used according to the presentinvention to achieve recognition of molecules which only have closerelatives but not identical structures already represented in theknowledge base of the neural network, such as, for instance,oligosaccharide substructures. Several neural networks, each designed toaccommodate one class or family of oligosaccharide spectra, can be used.

According to another aspect of the present invention, neuralnetwork-based systems may be used to identify partially methylatedalditol acetates (PMAAs) derived from complex carbohydrates from gaschromatography-electron impact mass spectra. Neural networks can beeasily trained to recognize the electron-impact mass spectra ofpartially methylated alditol acetates. These derivatives are used todetermine glycosyl linkage positions. Neural networks that include gaschromatographic retention times of the derivatives in the input data maybe used to enhance the recognition of molecular chirality. Neuralnetwork systems according to the present invention have achieved partialrecognition of stereochemical differences from mass spectra--a task notpreviously achieved by scientists. A combination of mass spectroscopicinformation with GC retention times can provide redundancy in suchdetermination of stereoisomers. Additionally, to optimize the neuralnetwork for wide-ranging experimental conditions, ratios of peakintensities in GC-MS data may be analyzed.

The ability of neural networks to achieve recognition of partiallymethylated alditol acetates may be enhanced by training them with scansfrom the MS originating from different GC injections. The variationscontained in a training set of MS scans from different GC injectionsincrease the neural network's tolerance to such variations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic representation of a neural network which containsan input layer, a hidden layer and an output layer of neurons.

FIGS. 2A-2F shows 500 MHz ¹ H-NMR spectra of six sugar alditols fromwhich information was presented to and identified by neural networks asdiscussed in Example I, below.

FIGS. 3A-3F shows plots of the synaptic weights of the inputlayer-hidden layer connections of a neural network which was trained asdiscussed in Example I with spectral information from the six sugaralditols.

FIG. 4 shows structures of 13 complex oligosaccharides which wereidentified by neural networks according to the present invention asdiscussed in Example II.

FIGS. 5A-5B shows input patterns generated from 500 MHz ¹ H-NMR spectraof structures 9 and 12, as illustrated in FIG. 4.

FIGS. 6A-6B shows examples for the generation of input patterns as shownin FIG. 5, and as discussed in Example II, from the spectra ofstructures shown in FIG. 4.

FIG. 7 shows a synaptic weight plot of the connections between inputneurons and one hidden layer neuron of a neural network (NN-1) discussedin Example II.

FIG. 8 shows structures of 22 partially methylated alditol acetateswhich were identified by neural networks according to the presentinvention as discussed in Example III.

FIGS. 9A-9B shows selected mass spectra of structures 21 and 29 shown inFIG. 8.

FIG. 10 shows a synaptic weight plot of the connections between inputneurons and one hidden layer neuron of a neural network discussed inExample III.

FIG. 11 shows structures of 14 complex oligosaccharides entered into aneural network training set according to the present invention, and twotest materials whose close relatives among the oligosaccharides wereidentified.

DETAILED DESCRIPTION Example I

Alditol spectra were subjected to analysis using neural networksaccording to the present invention as a preliminary procedure becausealditols exhibit a variety of characteristics including (i) highlydegenerate spectra (i.e. galactitol), (ii) different numbers of protonsin some of the spectra (i.e. pentitols vs. hexitols), (iii) spectra withall of the signals resolved and of first order (i.e. arabinitol), and(iv) spectra with no individually resolved signals of high order (i.e.ribitol).

A three-layered feedforward neural network using a back propagationlearning scheme as described in D. E. Rumelhart, et al., ParallelDistributed Processing, ch. 8 (MIT Press, 1986) was used. Thearchitecture is shown in FIG. 1. The network includes an input layer of400 input neurons, each of which feed a layer of 6 hidden neurons, whichin turn feed an output layer of 6 output neurons. The input layerneurons simply transmit their input values as output. The output o_(j)of the j'th hidden- or output-layer neuron is given by the logisticsemilinear activation function:

    o.sub.j =[1+exp (θ.sub.j -Σ.sub.i w.sub.ji * o.sub.i)].sup.-1 ;

where i and j represent neurons of two different layers with jrepresenting the higher layer; θ_(j) is a bias value for neuron j, andw_(ji) represents the synaptic strength or weight of the connectionbetween neuron i and neuron j. The output of neuron "j" is the result ofapplying the sigmoid-type threshold function corresponding to the neuronto the input received by the neuron from each neuron in the hiddenlayer. o_(j) approaches 1 when the net input is positive ("on") whileo_(j) approaches 0 when the net input is negative ("off"). For eachspectrum presented to the network, a single unique output layer shouldbe turned "on" and all the others turned "off." The knowledge of thenetwork is embedded in the values of the weights and biases, andteaching the network is reduced to finding a set of weights and biasesthat perform the required mapping of a set of input patterns onto acorresponding set of target output patterns.

A back propagation training scheme was used, that is, supervisedtraining based on repeated presentation to the network of a set oflearning patterns together with the desired output responses. At thebeginning of a learning session, the weights and thresholds wereinitialized with random numbers. For each pattern presented, the errorback-propagation rule defined a correction to the weights andthresholds, using a gradient descent method as described in chapter 8 ofthe Rumelhart reference cited above to minimize the square sum of thedifferences between target and actual outputs. The training proces wasrepeated iteratively until the difference between target and actualoutput fell below a predetermined specified level.

A total of 24 spectra were produced from the free induction decays(FIDs) of six sugar alditols, in which four sets of each spectrum wereobtained at a different line width, level of noise, apodization, and/orbaseline correction. The variation of noise level, line width, andbaseline distortion represented differences commonly found inexperimentally obtained ¹ H-NMR spectra. The 500-MHz ¹ H-NMR spectra ofsix sugar alditols were recorded at a concentration of 6 mg in 0.5 ml99.98% D₂ O, using acetone as an internal reference (2.2 ppm). The sixFID's were recorded in 8K datapoints using a spectral width of 2500 Hz,which resulted in a digital resolution of 0.61 Hz per point. Eight scanswere collected for each FID. From the six FID's, a total of 24 spectrawere produced, as four sets of six spectra, each set having a differentapodization and/or baseline correction. The spectra were transferred toan IBM PC, from the ASPECT 3000 computer of the Bruker AM 500spectrometer which was used, via the SPECNET facility of the standardspectrometer software. The PC employed a conventional communicationprogram and handled all subsequent data processing.

A reference learning set of 24 patterns was created, taking from eachspectrum 400 datapoints in the range 4.0 to 3.5 ppm, at a fixed distancerelative to the acetone line. The target output patterns were alsoincluded, and all patterns were normalized to have an integratedintensity proportional to the expected number of protons. The spectra ofthe six alditols included in the learning set are shown in FIG. 2.

The network converged to the desired solution in less than 30presentations of the 24 patterns. The presentation sequence wasrandomized, and for each pattern the weight corrections were appliedonly if the discrepancy between the actual and target output patternexceeded the specified level of accuracy. This method enhanced the rateof convergence, presumably because the weights were allowed to adjustmore freely for patterns not yet learned, while avoiding corrections forpatterns already learned with sufficient accuracy. Each output layerneuron was required to have an output value of 0.9 in order to be "on"and a value of 0.1 to be "off." The computations required to learn the24 spectra were not demanding; a typical learning session was completedin about 20 minutes, using a 6-MHz IBM PC AT with a math coprocessor.

Each of the six hidden layer neurons was connected to all of the inputlayer neurons, thus "seeing" the whole spectrum. As a result, thesynaptic weights of the connections between each input layer and eachhidden layer neuron were critical in determining which spectral featuresthe network used to identify a particular alditol (although the synapticweights of the hidden and output layer neurons obviously also played apart in such identification). Plots of the 400 synaptic weights for theinput and hidden layer connections for each alditol, which correspond tothe FIG. 2 spectra for the alditols, are shown in FIG. 3. The patternsclearly share common features with the alditol spectra shown in FIG. 2.The hidden layer neurons may thus be considered as detectors forspecific spectral features, as for example, a combination of multiplets.The nature of the sigmoid-type neuron output equation is such that whenspectral features of the spectrum coincide with similar features in thesynaptic weight pattern, the hidden-layer neuron in question tends toturn "on." The output layer combines the partial evidence from thehidden layer feature detectors to perform the final identification ofthe individual spectra.

The training process was repeated approximately twelve times, usingdifferent sets of initial random weights. In all cases, the weightpatterns that connect the hidden layer neurons to the input neuronsshowed similar recognizable features as presented in FIG. 3; i.e.subsets of the multiplets that form the original spectra. Thus, thecharacteristic signals of the individual spectra were represented indifferent weight traces. Experiments were also performed with networkshaving from three to ten neurons in the hidden layer. In each case, thenetwork was able to learn. However, learning was very slow when therewere only three hidden-layer neurons.

The neural network was tested for sensitivity to distortions in thelearned alditol spectra. The learning set of 24 spectra was used tocreate four additional sets of 24 test spectra. The first such set wasobtained by shifting each data point one increment left. The second setwas obtained by shifting each data point one increment right. The thirdset included added Gaussian-distributed noise, and the fourth set wasobtained by reducing the intensity by a factor of two. The Gaussiannoise resulted in a reduction of the signal-to-noise ration of thestrongest lines in the spectra from approximately 250:1 to 35:1.

The four sets of distorted test patterns were presented to the networkwhich had been "trained" with the original set of 24 unadulteratedspectra. The network correctly identified all of the left-shiftedspectra of the first test set. Two of the patterns from theright-shifted second set failed to turn on the proper output neuronfully, but only by a margin of 0.01 from the required 0.9 value(0.9±0.1). The network correctly identified all noisy set three spectra.It failed, however, to identify correctly any of the spectra from thefourth set, instead showing intermediate output neuron values in therange 0.64 to 0.79. The network when trained with the four test sets (inaddition to the original spectra) in a new training session, readilyidentified all four test sets of spectra correctly.

Example II

Complex oligosaccharides were analyzed using a large neural network. Forpurposes of this discussion, complex ¹ H-NMR spectra are defined ashaving only a small percentage of the signals separated intoindividually resolved NMR multiplets, i.e. most of the signals in thespectra are contained in a region of strong overlap, called the humpregion (see FIG. 5). This kind of spectrum is typical for mostbiologically important molecules such as DNA, RNA, proteins, and complexoligosaccharides. All these molecules are composed of many closelyrelated structures that give rise to very similar resonances in NMRspectra. NMR spectra of xyloglucans were chosen (FIG. 5) in order toexplore the abilities of artificial neural networks to recognize thesespectra. The molecules in the test set were composed of three to twentyglycosyl residues. A number of the residues in each xyloglucanoligosaccharide are identical, e.g. eight β-(1-4) linked glycosyl andsix α-(1-6) linked xylosyl residues in structures 10-13 as shown in FIG.4. Thus, major portions of the NMR spectra of these compounds aredetermined by repetitive residues which lead to high degeneracies in the¹ H-NMR spectra.

The 500 MH_(z) ¹ H-NMR spectra of compounds 1-13 as shown in FIG. 4 werepreviously recorded without any idea that they would be analyzed byneural networks according to the present invention. Accordingly, nospecial care was taken or needed when recording the spectra for trainingthe artificial neural network. The free induction decay (FID) files ofthese previously recorded spectra were retrieved from tape and processedin the following way: First, the FID files were Fourier transformedwithout any preprocessing. The resulting spectra were then normalized toa digital resolution of 0.5 H_(z) /point by interpolation of thespectral intensities. The chemical references of all spectra were alsonormalized to the same standard (acetone at 2.225 ppm). Three spectralregions from 1.15-1.34 ppm, 3.23-4.68 ppm and from 4.90-5.37 ppmcovering all the signals in the ¹ H-NMR spectra of structures 1-13 asshown in FIG. 4 were extracted from the spectra and combined intopatterns that were presented to the input neurons of the neural network(see FIG. 6). The total width of these combined regions was 1056 H_(z).The residual water signal at 4.748 ppm was replaced by zeros to avoidproblems with its greatly varying intensity and width. The signalswithin the spectra were normalized such that the intensities of alllines belonging to one selected hydrogen atom summed to 1. This processrequires the identification within the spectra of only one peak thatbelonged to a signal with known multiplicity and which is not overlappedby other signals. Usually, one of the anomeric signals was chosen toscale the intensity of the spectra. This approach feeds, for allspectra, approximately the same relative intensity into the neuralnetwork. Previous work suggested that the networks are not particularlysensitive to changes in intensities, which implies that the use of peakintegrals is not required for scaling purposes. Examples of the neuralnetwork input patterns that were obtained after the preprocessing of the¹ H-NMR spectra are displayed in FIG. 6.

A standard feed forward-back propagation neural network as mentionedabove and described in D. E. Rumelhart, et al., Parallel DistributedProcessing, ch. 8 (MIT Press, 1986) was used for the analysis of thespectra of the 13 xyloglucan oligosaccharide structures. Severalarchitectures of artificial neural networks were trained, which differedin the number of input, hidden layer and output neurons. The "on" stateof an output neuron was defined to be any neuron with an activation of0.9±0.1. Similarly, the "off" state of an output neuron was defined tobe any neuron with an activation of 0.1±0.1. The neurons were consideredto be in an undefined state with activation values between 0.2 and 0.8.

The initial training set containing the NMR spectra of oligosaccharides1-13 was used to train a neural network with 2113 input, ten hiddenlayer and thirteen output neurons (NN-1). Each input neuron represented0.5 H_(z) of the NMR spectral (total 1056 H_(z)). After training of thenetwork, plots of the synaptic weight of input and hidden layer neuronconnections were used to analyze the convergence properties of thenetwork. A sample weight plot of such synaptic weights for one hiddenlayer neuron of NN-1 is shown in FIG. 7. It is apparent that both theanomeric region (from 4.47 to 5.37 ppm) and the hump region (from 3.23to 4.21 ppm) contribute heavily to the recognition capabilities of theneural network. If the corresponding signals are present in the inputpatterns, positive weights increase the activation of hidden layerneurons whereas negative weights contribute to turning the hidden layerneurons off. Comparisons of all input layer-hidden layer connectionsynaptic weight plots for each hidden layer neuron with the actualspectra revealed that almost all signals in the original ¹ H-NMR spectraof structures 1-13 were utilized by the neural network to activate or todeactivate the hidden layer neurons.

¹ H-NMR spectra of the same compound are no more reproducible than thedata of other analytical techniques. It was expected that training theneural network with deliberately imperfect data would increase theability of the neural network to correctly recognize spectra whichinherently contain similar imperfections. In order to test thishypothesis, "fuzziness" (that is, minor variations in the input data)was introduced into the training set. This was accomplished bygenerating four additional copies of each spectrum. One copy containedthe original spectrum with all signals shifted 0.5 H_(z) to the right,one 1.0 H_(z) to the right, one 0.5 H_(z) to the left and one 1.0 H_(z)to the left. Such procedures enable neural networks to be more tolerantof the minor changes in chemical shifts and line width that occur whenspectra of the same molecule are obtained at different times or ondifferent instruments. The initial training set of thirteen spectra wasexpanded to 65 by including the fuzzy spectra. This dataset was thenused to train NN-1. Once again the neural network converged and was ableto recognize the spectra of structures 1-13. The root mean square errorof the trained neural network was 0.03 indicating an excellent agreementbetween target and actual output patterns.

The success of the structural reporter group concept demonstrates thatsignals whose chemical shifts are outside the poorly resolved humpregion (˜3.2 to 4.2 ppm) can be used to successfully recognize thespectra of a variety of oligosaccharides. The ability of neural networksto use only the structural reporter group signals on the one hand andonly the hump region signals on the other hand in order to recognizespectra was accordingly tested. The ¹ H-NMR spectra of structures 1-5was split into two sets. One set contained only the signals of thestructural reporter groups of structures 1-5, that is, the regions from4.47 to 5.37 ppm and from 1.15 to 1.35 ppm. The second set of partialspectra of structures 1-5 contained only the signals in the hump region,that is from 3.23 to 4.21 ppm. The spectral resolution was maintained at0.5 H_(z) /input neuron. A neural network with 1003 input, 5 hidden, and5 output neurons was used for the structural reporter group region, anda neural network with 981 input, 5 hidden, and 5 output neurons was usedfor the hump region. Even with only these partial data sets, both neuralnets converged and were able to recognize each of the spectra. This wasexpected for the structural reporter group region; the result with thehump region was less intuitive and very instructive. This resultstrongly suggests that trained artificial neural networks candiscriminate between spectra--even if trained with NMR spectralinformation that shows few, if any, evident differences to the humanobserver. Although it was not obvious that artificial neural networkscould discriminate between the poorly resolved signals in the humpregion, it is apparent that the hump region contains the informationnecessary to discriminate between oligosaccharides. The result meansthat the artificial neural network is better able to achieve this goalthan humans.

It became evident from comparison of these network models to othernetwork models with three (NN-2) or ten hidden (NN-3) layer neurons,respectively, that the `signal to noise` ratio of the weight patterns asrepresented in FIG. 7 increased with decreasing number of hidden layerneurons, which can affect recognition stability and discrimination powerof the neural networks.

This example thus establishes that a feedforward propagation artificialneural network is able to distinguish between the ¹ H-NMR spectra ofoligosaccharides that differ by only one glycosyl residue out of 20. Aneural network of the type described can thus form the core of a patternrecognition system to recognize ¹ H-NMR spectra. In contrast totraditional rule-based expert systems, neural networks discriminatebetween spectra without requiring the researcher to "hard-code" a set ofrules. Teaching new spectra to the network involves adding the newspectra to the learning set and repeating the learning process. Theseresults suggest that neural networks can be used to recognize the verycomplex ¹ H-NMR spectra of most if not all biologically interestingcomplex carbohydrates or other materials or compounds of interest.

Example III

Structure elucidation of a complex oligosaccharide structure normallybegins with the determination of its glycosyl-residue andglycosyl-linkage composition. Analysis of the glycosyl-linkage patternis made by comparison of the gas chromatographic retention times andelectron impact mass spectra of the partially methylated alditol acetatederivatives (PMAAs) of the glycosyl residues. Glycosyl-linkage analysisinvolves the per-O-methylation of the oligosaccharide being analyzed,followed by hydrolytic cleavage of its glycosidic linkages in order togenerate a mixture of partially methylated monosaccharides. The carbonatoms previously involved with other glycosyl residues or ring formationnow carry hydroxy functions, while the carbon atoms in the originaloligosaccharide that had free hydroxyl group now are substituted withO-methyl groups. Reduction of the partially methylated monosaccharideswith sodium borodeuteride yields the corresponding alditols carrying adeuterium atom at the former aldehyde or keto function. Subsequentacetylation protects the unsubstituted hydroxy functions with O-acetylgroups.

Gas chromatographic separation of the resulting partially methylatedalditol acetates (PMAA) followed by an electron impact massspectrometric analysis of the individual PMAAs allows the location ofthe O-methyl and O-acetyl substituents to be ascertained. Discriminationbetween stereoisomers (e.g. glucitol, galactitol and mannitol) isachieved by comparison of GC retention times to those of known PMAAderivatives. In other words, the mass spectrum is used to identify theparent alditol without taking the stereochemistry into account and thegas chromatographic retention time is subsequently used to assign thestereochemistry of the molecule.

Chemical analysis of the glycosyl-residue and glycosyl-linkage has theadvantage over NMR spectroscopic analysis in that a much smallerquantity (˜10 ug compared to ˜200 ug) of the oligosaccharide is needed.Such interpretation of the spectral data is not only costly in terms ofthe amount of sample required but is both complicated andtime-consuming. This is true because the total number of different PMAAstructures that can be obtained from one hexose is 64. Taking alldifferent naturally occurring sugars into account, spectroscopists mustdetermine from several thousand possible PMAA derivatives which moleculeis present.

Accordingly, the mass spectra of PMAA derivatives of xylitol,arabinitol, rhamnitol and fucitol (see FIGS. 8 and 9) were used to testthe ability of an artificial neural network to recognize the massspectra obtained by combined GC-MS. An HP 5890 gas chromatograph with a5970 mass selective detector was used for separation and quantitation ofthe compounds. The spectra were recorded and stored on an HP 9000 series200 workstation. They were subsequently transferred to a DECstation 3100for further processing. In order to use the spectra as input to theneural network, all data within each spectrum were normalized relativeto the largest peak in the spectrum. Each mass to charge ratio wasrounded to an integer number. Sets of network input patterns were thengenerated by mapping the normalized abundance for each mass number in aspectrum to the corresponding position of an input neuron. Spectratypical of those used in this study are shown in FIG. 9.

The neural network software used was the back propagation program ofRummelhart and McClelland, described in J. McClelland and D. E.Rumelhart, Explorations in Parallel Distributed Processing (MIT Press,1988), which is incorporated herein by this reference. All the networkmodels reported here consisted of an input layer of 400 neurons, ahidden layer of either 5, 15 or 25 neurons and an output layer of 22neurons.

An initial set of patterns with which to train the network was createdby selecting the mass spectra of twenty-two well resolved peaks from thechromatographic data of four different PMAA mixtures. The structures ofthese molecules are shown in FIG. 8. This set included six pairs ofepimeric molecules (e.g. compounds 14 & 34, 16 & 35, 22 & 28, 23 & 31,25 & 32 and 27 & 33 of FIG. 8. Each chromatographic peak produced two tofive MS spectra of the single PMAAs which resulted in a training set of66 input patterns for the 22 different PMAAs. This training set was usedsuccessfully to train a network of 400 input neurons, 25 hidden neuronsand 22 output neurons to recognize each of the 22 PMAAs including theepimeric pairs.

In order to increase the tolerance of the neural network to variationsin the spectra, another training set was generated with variations inpeak intensities deliberately included. This was accomplished byincluding copies of the original spectra where the copies differed fromthe original in that each peak in each spectrum was multiplied by arandom factor ranging from 0.5 to 1.5. The network was successfullytrained to recognize all 22 compounds in this set. When spectra thatwere omitted from the training set were presented to the trainednetwork, eighteen of the twenty two compounds were recognized. The testspectrum for structure 14 activated its isomeric partner 34. When thespectrum from either structures 25 or 32, which are isomers, waspresented, the network outputs for both 25 and 32 were partiallyactivated. A single input pattern failed to cause correct identificationof the proper molecule.

The ability to identify partially methylated alditol acetates as well asto discriminate between most of the stereoisomers from mass spectrademonstrates the powerful spectrum recognition and identificationcapabilities of artificial neural networks. These results show thatneural networks can be trained to identify all naturally occurringpartially methylated alditol acetates. While the above-described effortshave been focused on PMAAs, this neural network-based technique isreadily adaptable to the mass spectra of other types of compounds. Thisapproach can be generalized to provide researchers in differentlaboratories with the ability to build their own neural networks byforming training sets with their own mass spectra, training the neuralnetwork and these sets, and subsequently using the neural network toidentify the mass spectra of molecules pertinent to their work.Additionally, the researcher would get answers from the neural networksalmost instantaneously, as compared to longer library searches.

Discussion

Neural networks are clearly useful to recognize very complex spectra andto deal with variations occurring in experimental spectra while stillmaintaining the necessary discrimination between spectra. They alsotolerate changes in chemical shifts of individual signals, changes innoise, and changes in line width and line shape.

Enhancing Pattern Recognition of NMR Spectra

In order to accomplish the foregoing tasks effectively, however, neuralnetworks must be able to accommodate changes in several variableswithout affecting the recognition of the spectra. The variableparameters include the intensity of the spectrum (sample size), thedigital resolution of the spectrum, the presence of solvent signals, thepresence of signals from impurities (e.g. buffers), background noise,line width of the signals, and the presence of internal standards. Theabove-described examples show that neural networks can cope with atleast some of these spectral variables: moderate variations in noiselevels, chemical shifts, and intensities. Changes in absoluteintensities may be handled by normalizing the spectra before they arepresented to the neural network. Difficulties with S/N ratios can beminimized by including noise in the neural network's training set. Thetolerance of the neural network to the presence of solvent signals ofvarying intensity or residual signals from buffers is less certain. Oneway to circumvent this problem will be for users to delete solventsignals from the original spectrum before presenting that spectrum tothe trained neural network. A similar approach may be needed in order tosolve problems with residual signals from buffers. In situations whereelimination of solvent and impurity peaks involves the removal of asignal that is part of the target molecule, the result may be reducedability of the neural network to recognize the spectrum. However, thiswill be minimized by inclusion of the spectrum without the solvent orimpurity signals in the training set.

The presence of different internal standards in experimental spectra andvariations in digital resolution, that is the frequency difference ofsuccessive points in the experimental spectrum, must be solved in otherways. The difficulties arising from use of different internal standardsmay be addressed by using a recently published approach that createstranslationally invariant neural networks. See, A. Fuchs and H. Haken,60 Biol. Cybern 107 (1988). With this type of neural network the inputpattern does not have to be presented to the same neurons but can berecognized at different input layer positions. Different digitalresolutions of spectra can be addressed by establishing a standard forthe data presented to the network; all spectra that were not recorded atthe standard resolution can be easily preprocessed using aninterpolation algorithm to generate the standard resolution. Thistechnique was in fact used to normalize complex ¹ H-NMR spectra to adigital resolution of 0.5 Hz in preliminary studies.

Reduction of Data Amount for Input into the Neural Network

Various techniques may be employed to reduce the amount of data requiredby neural networks in order to recognize complex oligosaccharides. As anexample, the input layer of such a network must presently cover thespectral range extending from ˜0.5 to ˜8.0 ppm, that is, a range of 3750Hz in a 500-MHz NMR spectrum. If the spectrum is fed to the network atits normal digital resolution of about 0.2 Hz per point, a large numberof input neurons (22,500) would be required. This would require moreprocessing to update the weights and thresholds than with the 2113 inputneurons we have been using. Additionally, the number of hidden layerneurons would need to be increased in order to cover a large number ofpossible spectra to be recognized. Since the processing time for onetraining cycle is approximately proportional to the product of input andhidden layer neurons, even if the spectral resolution were reduced to0.5 Hz/neuron, 7500 input neurons would be required in order to coverthe spectral range of interest.

A promising alternative approach is to use the network to analyze freeinduction decays ("FIDs") rather than transformed spectra. FIDs containthe same information as transformed spectra. The advantage of using FIDsrather than transformed spectra is that an FID can easily be convertedto the desired number of inputs without losing information from thespectrum. Again, different digital resolutions could be normalized byinterpolation of the data points. Using the FIDs as the training set forthe neural network may avoid some of the problems addressed above (e.g.,digital resolution, shifted spectra due to different internalreferences), but other problems arise. An FID is composed of decayingcosine frequencies that result in spectral lines after Fouriertransformation. The observed frequency is the difference between theactual resonance frequency of that nucleus and an internal carrierfrequency. Thus, if two spectra of the same compound are recorded withdifferent carrier frequencies, the FID's are completely different. Thecarrier frequency varies for spectra obtained on different instrumentsor even by different operators on the same instrument. This problem maybe addressed by using a spectral preprocessing algorithm that involves(i) a complex forward-Fourier transformation, (ii) correction of theoffset in the transformed spectrum by shifting the spectrum, and (iii)an inverse Fourier transformation to regenerate the FID with anormalized carrier frequency. Using a combination of transformed spectraand FIDs as the input to the neural network could prove to be morepowerful than using either of these data separately.

Most complex carbohydrate ¹ H-NMR spectra are recorded on a 500 MHzinstrument. Different spectrometer frequencies impose a problem for theneural network analysis. The pattern of most peaks within the ¹ H-NMRspectra do not change from one carrier frequency to another. However,the distance between these subpatterns does vary and consequently, thischanges the appearance of the spectra. One alternative is obviously todesign the neural network to handle only spectra recorded at onefrequency, such as 500 MHz, as comparing spectra recorded at otherfrequencies will complicate the analysis. The problem of differentspectrometric carrier frequencies could be overcome by training thenetwork to recognize the subpatterns of individual H-atoms, e.g.,singlets, doubles, triplets, or doublet-doublet multiplets. In otherwords, the network may recognize the multiplicity of signals which isthe most detailed feature of a spectrum. The composition and location ofthe subpatterns could then define the chemical structure. For example,the neural network may be able to be taught with just the isolatedmultiplet structures found in oligosaccharides before proceeding to theteaching of monosaccharides and oligosaccharides. This approach, ifsuccessful, would eliminate having a separate neural network for eachspectrometer frequency. Such networks may require more than one hiddenlayer to handle the individual steps required for detection of multiplefeatures, however. This approach could also provide substructureinformation from the interpretation of the neural networks even in caseswhere the presented structure does not exactly match the informationstored in the neural network.

Oligosaccharide structures form families of molecules.Functionally-related molecules often vary in only a few residues. Neuralnetworks may also be able to recognize the glycosyl residues that makeup an oligosaccharide. This would imply that, even if the structurecurrently presented to the neural network is not contained in theknowledge base, the neural network would be able to assign the glycosylresidues in the oligosaccharide. A way to "prime" the neural networkwith substructure information could be derived by using spectra ofsubstructures extracted from the spectra of higher oligosaccharides asstarting weight values rather than randomizing the initial weight set.The number of substructures would represent a much smaller dataset thanthe complex structures. One way to test this "priming" of the neuralnetwork is by using the subspectra of all component glycosyl residueswith certain linkage patterns. These subspectra can be extracted fromthe total spectrum by using 1-D or 2-D HOHAHA spectra which, uponirradiation of one signal of one subunit, deliver all the signals withinthat subunit as a separate trace. Initializing the weights with thesesubspectra would result in the activation of the hidden layer neuronsconnected to this pattern whenever this subunit is present. A neuralnetwork with this feature may have two hidden layers where one hiddenlayer represents the substructure information.

A neural network that can recognize the individual building blocks(glycosyl residues) of all oligo- and polysaccharides could inform theresearcher of the probable glycosyl and non-glycosyl compositions of theunknown spectrum with the spectral knowledge base of the neural network.No other rapid analytical procedure can simultaneously provide theglycosyl composition and anomeric configurations.

Additionally, networks according to the present invention may be used togenerate information that is useful to indicate and identify materialswhose spectra have not been included in the training set, by indicatingclose relatives of the material. For example, referring to FIG. 11,presenting the spectrum of the mixture of the molecules T1 and T2 shownat the bottom of that figure to the neural network described in Example2 above, yielded partial activation (activation levels between 0.2 and0.8) of compounds 2, 3, 4, 6a and 6b. Each of those structures deviatesonly in one glycosyl residue from the mixture of tested structures T1and T2.

Other Network Architectures

The neural network schemes described above represent supervisedlearning, because the manager of the neural network must define theresponse of the output layer for each spectrum in the training dataset.In other neural network schemes, like the Adaptive Resonance Theory ofGrossberg discussed above and in G. A. Carpenter and S. Grossberg, 37Computer Vision, Graphics and Image Processing 54-115 (1987), thearchitecture of the network is pre-defined, but the output is not set toa pre-defined value.

Data Modification For Enhanced Performance

A neural network's tolerance to spectral data variations and anomaliesand thus its performance can be enhanced by manipulating the originalFIDs of the recorded spectra. Tolerance to variations in line widths,S/N ratios, and chemical shifts can be obtained by mathematicallymodifying the original data. Line width can be modified by changing thedecay rate of the FID. S/N ratio can be changed by adding white noise tothe FID. Chemical shifts can be changed by shifting the transformedspectrum left and right. Presenting such an increased set of spectra tothe network in a learning session will improve its ability to deal withthese variations.

Signal overlap in the hump region of the spectrum increases as thecomplexity of the molecule increases. In order to get a satisfactorylevel of discrimination between closely related structures,resolution-enhanced spectra may prove to be necessary for neural networkanalysis. The difference between normal and resolution enhanced spectrawill not be important in the regions of spectra where the well-definedsignals of the structural reporter groups are contained. However, if thecomplex hump region of the spectrum can be resolved into individuallines, the spectrum may be more easily recognized by a neural network.

Performance

Neural networks have the advantage of being able to quickly compare(less than 0.5 sec) a newly recorded spectrum to the informationcontained in the training set of the network. Thus, a routine user wouldrapidly receive answers to queries of the knowledge base contained inthe neural network. The training procedure consumes more time, but thatis not a problem for the user, as any new information added to theneural network is handled by one person, and new spectra could be addedin a net training session during "off-hours". It is likely to beeffective to divide the neural networks into sets of spectrarepresenting different types of complex carbohydrates, i.e.,glycoproteins, glycolipids, glycosaminoglycans, bacterialpolysaccharides, fungal polysaccharides, plant cell wallpolysaccharides, and so forth. Each sub-network would have its ownassociated neural network, each of which could analyze its respectiveknowledge base more accurately than one network could store theinformation of all combined datasets. The user would always know thesource and type of molecule he/she is tryinq to match and could checkagainst any of the neural networks when required.

Recognition Of Two-Dimensional Data

Neural networks also appear to be useful in analyzing 2-D NMR spectra;such analysis is useful because it can be of great value in cases wherethe neural network of the 1-D spectra will not be able to give anunambiguous answer. Although 2-D spectra have the advantage of greaterdispersion of the spectral information, that is, into two dimensions,they require about tenfold more sample to record, and sample size isoften limited in biological samples. The same neural network conceptsdescribed for 1-D spectra may be used to interpret 2-D NMR spectra, butthe input layer is extended into two dimensions. Standard COSY spectrarecorded in magnitude mode may be employed so that all of the NMRsignals have positive values. The network may also be used to recognizephase sensitive COSY spectra. This requires a different set of weightsand thresholds in the neural network, because the network mustaccommodate both positive- and negative-intensity information in a 2-Dcontour map. Use of a neural network in this application is analogous inmany senses to use of neural networks to recognize subpatterns of aphotograph that had different positions within the picture. See, e.g.,A. Fuchs and H. Haken, 60 Biol. Cybern. 17, 107 (1988).

Neural Network-based Pattern-Recognition of Mass Spectra

It has been discussed above that artificial neural networks can be usedsuccessfully to recognize the mass spectra of partially methylatedacetylated alditols. Their efficiency in recognizing PMAAs may beenhanced in several ways. Including the gas chromatographic retentiontime in the data presented to the input neurons is one way of improvingthe precision of recognition. Two different possibilities for theimplementation of the GC retention times in the neural network exist. Aset of input neurons that represents both the mass spectrum and the GCcan be fully connected to the all hidden layer neurons. With thisapproach the GC retention time data influences the neural network outputapproximately as much as any of the mass spectrometric fragment peaks.If this is too little emphasis for the GC retention time, another optionis to connect the GC related input neurons to a separate set of hiddenlayer neurons and connect all hidden neurons fully to the outputneurons. The latter approach assures that the GC data influences therecognition of the spectra as much as the combined effect of the MSfragment peaks. The implementation of the GC data into the neuralnetwork requires the standardization of the retention times. This may beaccomplished by adding a set of five standards to each GC-MS analysis.Interpolation of the retention times of the peaks between pairs of thestandards provides the normalized retention times for input into theneural network.

Normally, the mass spectra of PMAAs are interpreted to determine thesubstitution pattern of O-methyl and O-acetyl groups but not theirstereochemistry. The training set of 22 PMAAs discussed above containssix pairs of epimers. Each pair of epimers has an identical substitutionpattern. From these six pairs the neural network correctly identifiedthe stereochemistry of four pairs. One epimeric pair (25 & 32) gavepartial activation of the pair's associated output neurons when thespectrum of either 25 or 32 was presented to the neural network. Thus,one can differentiate between most of the identically substitutedstereoisomers but not all. Comparison of the mass spectra of identicallysubstituted stereoisomers shows that the use of intensity can beimportant in order to fully distinguish between them. The solution seemsnot to be the interpretation of the intensity ratios of one or two pairsof peaks but the intensity ratios of the majority of the peaks. However,work on recognizing ¹ H-NMR spectra suggests that neural networks arenot very sensitive to changes in intensity. In order to enhancerecognition of intensity differences, a different neural network may beused that has a two dimensional input. One dimension represents the massunit of the ion and the second dimension containing several neurons permass unit represents the intensity of the peak. Each intensity unit willrepresent a certain threshold for the normalized intensity and will beturned on if the intensity of the peak is higher than the threshold.This should cause the neural network to be more sensitive to theintensity of the peaks.

Another way to enhance the ability of the neural network to discriminatedifferent stereoisomers without adding another input dimension to theneural network is to use highly standardized input data. Implementationof a two step process, each of which would be based on a neural network,may achieve the goal. The first step can reveal, by partial activationof the target output neurons, that a stereoisomer problem exists, whichwould have to be examined in greater detail in a second step of neuralnetwork analysis. The second step makes use of auto-associative neuralnetworks which are capable of restoring a partially distorted patternaccording to the training set that is included in the weights andthresholds of this kind of network. Thus, each stereoisomer can have itsown auto-associative neural network. Activation of the output tells thescientist that this neural network is recognizing the input pattern asbeing its target pattern and thus assign the stereochemistry of thisPMAA. As an example, eight separate auto-associative neural networks areneeded to find which stereoisomer is present for hexitols.

Neural networks capable of interpreting GC-MS data have broadapplications including metabolite studies, environmental trace analyses,and assays of biological samples. The advantage of using a neuralnetwork for the analysis of data rather than a library search is theease with which the neural network can be tailored to the needs of theresearcher and the speed with which the knowledge base of the neuralnetwork will give an answer. Furthermore, it does not rely on humandefinition of deviations within the data from one experiment to the nextbut incorporates these differences from the training set.

The foregoing discussion is provided for purposes of illustration andexplanation of embodiments of the present invention, including apreferred embodiment. The above examples, which discuss the use ofneural networks to identify complex carbohydrate molecules from theirspectra, show the principles that allow such identification of anymaterial or structure from which a spectrum or spectra can be obtainedthat can be applied to a neural network. Accordingly, the foregoingdiscussion is not intended to be limiting in nature, and it will beapparent that uses of various types of neural networks (whether one ormore, and whether embedded in software, hardware or a combination),various types of spectra (whether NMR, IR, GC, MS or other), andidentification of any conceivable type of material from which arepresentative spectrum can be obtained, fall within the scope andspirit of the invention.

What is claimed is:
 1. Apparatus for analyzing and identifying thestructure of a particular organic material by recognizing patterns ofinformation that are characteristic of such materials, the apparatuscomprising:(a) analytical means for applying energy to the organicmaterial under analysis, sensing transformations in the energy impartedby the material, and producing therefrom spectral informationcorresponding to the energy transformations and the structure of thematerial; (b) means for digitizing a plurality of incremental portionsof the spectral information into digital data; and (c) off-line neuralnetwork means for utilizing the digital data to identify the structureof the organic material under analysis, comprising;(i) an input layercomprising a plurality of input nodes, each of which nodes receives thedigital data; (ii) an output layer hierarchically lower than the inputlayer comprising a plurality of output nodes, for indicating andidentifying the structure of the material under analysis; (iii) aplurality of synaptic connections, each of which connects a node in ahierarchically higher layer to a plurality of nodes in a hierarchicallylower layer and each of which features a synaptic strength value whichhas been generated during a back-propagation learning process usingspectral data from organic materials analogous to the organic materialunder analysis; and (iv) each of the nodes featuring a threshold valuewhich has been generated during a back-propagation learning processusing spectral data from organic materials analogous to the organicmaterial under analysis.
 2. Apparatus according to claim 1 in which eachnode of the output layer corresponds to an organic material. 3.Apparatus according to claim 1 comprising at least one hidden layerhierarchically intermediate the input and output layers and comprising aplurality of hidden nodes.
 4. Apparatus according to claim 1 in whichthe neural network means is in the form of at least one computerprogram.
 5. Apparatus according to claim 1 in which the nodes areimplemented in computer hardware.
 6. Apparatus according to claim 1 inwhich each of the output nodes corresponds to a carbohydrate molecule.7. Apparatus according to claim 1 in which the off-line neural networkmeans is a forward-feed neural network having a single hidden layer ofhidden nodes.
 8. Apparatus for analyzing and identifying the structureof a particular organic material by recognizing patterns of informationthat are characteristic of such materials, the apparatus comprising:(a)analytical means for applying energy to the organic material underanalysis, sensing transformations in the energy imparted by thematerial, and producing therefrom spectral information corresponding tothe energy transformations and the structure of the material; (b) meansfor digitizing a plurality of incremental portions of the spectralinformation into digital data; and (c) off-line neural network means forutilizing the digital data to identify the structure of the organicmaterial under analysis, comprising:(i) an input layer comprising aplurality of input nodes, each of which nodes receives the digital data;(ii) an output layer hierarchically lower than the input layercomprising a plurality of output nodes, for indicating and identifyingthe structure of the material under analysis; (iii) at least one hiddenlayer hierarchically intermediate the input and output layers comprisinga plurality of hidden nodes; (iv) a plurality of synaptic connections,each of which connects a node in a hierarchically higher layer to aplurality of nodes in a hierarchically lower layer and each of whichfeatures a synaptic strength value which has been generated during aback-propagation learning process using spectral data from organicmaterials analogous to the organic material under analysis; and (v) eachof the nodes featuring a threshold value which has been generated duringa back propagation learning process using spectral data from organicmaterials analogous to the organic material under analysis.
 9. Apparatusaccording to claim 8 in which each node of the output layer correspondsto an organic material.
 10. Apparatus according to claim 8 comprising aplurality of hidden layers of hidden nodes.
 11. Apparatus according toclaim 8 in which the off-line neural network means is in the form of atleast one computer program.
 12. Apparatus according to claim 8 in whichthe nodes are implemented in computer hardware.
 13. Apparatus accordingto claim 8 in which each of the output nodes corresponds to acarbohydrate molecule.
 14. Apparatus according to claim 8 in which theoff-line neural network means is a forward-feed neural network having asingle hidden layer of hidden nodes.
 15. Apparatus for analyzing andidentifying the structure of a particular organic material byrecognizing patterns in spectra that are characteristic of suchmaterials, the apparatus comprising:(a) spectroscopy analytical meansfor applying radiation energy to the organic material under analysis,sensing transformations in the energy imparted by the material, andproducing therefrom spectral information corresponding to the energytransformations and the structure of the material; (b) means fordigitizing a plurality of incremental portions of the spectralinformation into digital data; and (c) off-line neural network means forutilizing the spectral information to identify the structure of theorganic material under analysis, comprising:(i) an input layercomprising a plurality of input nodes, each of which nodes receivesdigital data corresponding to an incremental portion of the spectralinformation; (ii) an output layer hierarchically lower than the inputlayer comprising a plurality of output nodes, for indicating andidentifying the structure of the material under analysis; (iii) at leastone hidden layer hierarchically intermediate the input and output layerscomprising a plurality of hidden nodes; (iv) a plurality of synapticconnections, each of which connects a node in a hierarchically higherlayer to a plurality of nodes in a hierarchically lower layer and eachof which features a synaptic strength value which has been generatedduring a back-propagation learning process using spectral data fromorganic materials analogous to the organic material under analysis; and(v) each of the nodes feature a threshold value which has been generatedduring a back propagation learning process using spectral data fromorganic materials analogous to the organic material under analysis. 16.Apparatus according to claim 15 in which each node of the output layercorresponds to an organic material.
 17. Apparatus according to claim 15in which the material under analysis is a carbohydrate molecule and eachoutput node corresponds to a carbohydrate molecule.
 18. Apparatusaccording to claim 15 in which the spectroscopy analytical means is anuclear magnetic resonance spectroscopy device.
 19. Apparatus accordingto claim 15 in which the spectroscopy analytical means is an infraredabsorption spectroscopy device.
 20. Apparatus according to claim 15 inwhich the spectroscopy analytical means is an x-ray analysis device. 21.Apparatus according to claim 15 in which the spectroscopy analyticalmeans is a mass spectrometer.
 22. Apparatus according to claim 15 inwhich the spectroscopy analytical means is a gas chromatograph. 23.Apparatus according to claim 15 in which the spectroscopy analyticalmeans is ultraviolet spectroscopy.
 24. Apparatus for analyzing andidentifying the structure of a particular carbohydrate material byrecognizing patterns in spectra that are characteristic of suchmaterials, the apparatus comprising:(a) magnetic resonance analyticalmeans for subjecting the carbohydrate material under analysis to amagnetic field and radio-frequency radiation, and producing spectralinformation corresponding to the absorption of the radiation and thestructure of the material; (b) means for digitizing a plurality ofincremental portions of the spectral information into digital data; and(c) off-line neural network means for utilizing the spectral informationto identify the structure of the material under analysis, comprising:(i)an input layer comprising a plurality of input nodes, each of whichnodes receives digital data corresponding to an incremental portion ofthe spectral information; (ii) an output layer hierarchically lower thanthe input layer comprising a plurality of output nodes, for indicatingand identifying the structure of the material under analysis; (iii) atleast one hidden layer hierarchically intermediate the input and outputlayers comprising a plurality of hidden nodes; (iv) a plurality ofsynaptic connections, each of which connects a node in a hierarchicallyhigher layer to a plurality of nodes in a hierarchically lower layer andeach of which features a synaptic strength value which has beengenerated during a back-propagation learning process using spectral datafrom carbohydrate materials analogous to the material under analysis;and (v) each of the nodes featuring a threshold value which has beengenerated during a back propagation learning process using spectral datafrom carbohydrate materials analogous to the material under analysis.25. A method for analyzing and identifying the structure of a particularorganic material by recognizing patterns in spectra that arecharacteristic of such materials, comprising the steps of:(a) subjectingan organic material under analysis to energy in an analytical means; (b)sensing transformations in the energy imparted by the material; (c)producing spectral information corresponding to the energytransformations and the structure of the material; (d) digitizing aplurality of incremental portions of the spectral information; (e)supplying at least one off-line neural network which comprises:(i) aninput layer comprising a plurality of input nodes, each of which iscapable of receiving digital data corresponding to an incrementalportion of the spectral information; (ii) an output layer hierarchicallylower than the input layer comprising a plurality of output nodes, forindicating and identifying the structure of the material under analysis;(iii) a plurality of synaptic connections, each of which connects a nodein a hierarchically higher layer to a plurality of nodes in ahierarchically lower layer; and each of which features a synapticstrength value which has been generated during a back-propagationlearning process using spectral data from organic materials analogous tothe organic material under analysis; and (iv) each of the nodesfeaturing a threshold value which has been generated during a backpropagation learning process using spectral data from organic materialsanalogous to the organic material under analysis and (f) applying thedigital data corresponding to incremental portions of the spectralinformation relating to the material under analysis to the input nodesof the neural network in order to generate at the output nodesinformation that is useful to indicate and identify the structure of thematerial under analysis.
 26. A method according to claim 25 in which thestep of supplying at least one off-line neural network comprisessupplying at least one off-line neural network which includes at leastone hidden layer hierarchically intermediate the input and output layerscomprising a plurality of hidden nodes.
 27. A method according to claim25 in which the steps of subjecting the material under analysis toenergy in a spectroscopy analytical means, sensing transformations inthe energy imparted by the material and producing spectral informationcorresponding to the energy transformations and the structure of thematerial are performed using a nuclear magnetic resonance spectroscopydevice.
 28. A method according to claim 25 in which the steps ofsubjecting the material under analysis to energy in a spectroscopyanalytical means, sensing transformations in the energy imparted by thematerial and producing spectral information corresponding to the energytransformations and the structure of the material are performed using aninfrared absorption spectroscopy device.
 29. A method according to claim25 in which the steps of subjecting the material under analysis toenergy in a spectroscopy analytical means, sensing transformations inthe energy imparted by the material and producing spectral informationcorresponding to the energy transformations and the structure of thematerial are performed using an x-ray analysis device.
 30. A methodaccording to claim 25 in which the steps of subjecting the materialunder analysis to energy in a spectroscopy analytical means, sensingtransformations in the energy imparted by the material and producingspectral information corresponding to the energy transformations and thestructure of the material are performed using a mass spectrometer.
 31. Amethod according to claim 25 in which the steps of subjecting thematerial under analysis to energy in a spectroscopy analytical means,sensing transformations in the energy imparted by the material andproducing spectral information corresponding to the energytransformations and the structure of the material are performed using agas chromatography device.
 32. A method according to claim 25 in whichthe steps of subjecting the material under analysis to energy in aspectroscopy analytical means, sensing transformations in the energyimparted by the material and producing spectral informationcorresponding to the energy transformations and the structure of thematerial are performed using an ultraviolet spectroscopy device.
 33. Amethod according to claim 25 in which the generated informationindicates the material under analysis.
 34. A method according to claim25 in which the generated information indicates portions of the materialunder analysis.
 35. A method for analyzing and identifying the structureof a particular carbohydrate material by recognizing patterns in spectrathat are characteristic of such materials, comprising the steps of:(a)subjecting a carbohydrate material under analysis to energy in aspectroscopy analytical means; (b) sensing transformations in the energyimparted by the material; (c) producing spectral informationcorresponding to the energy transformations and the structure of thematerial; (d) selecting portions of the spectral information which aredesired for use in identifying the structure of the material; (e)digitizing a plurality of incremental portions of the selected spectralinformation; (f) supplying at least one off-line neural network whichcomprises:(i) an input layer comprising a plurality of input nodes, eachof which is capable of receiving digital data corresponding to anincremental portion of the spectral information; (ii) an output layerhierarchically lower than the input layer comprising a plurality ofoutput nodes, for indicating and identifying the structure of thematerial under analysis; (iii) at least one hidden layer hierarchicallyintermediate the input and output layers comprising a plurality ofhidden nodes; (iv) a plurality of synaptic connections, each of whichconnects a node in a hierarchically higher layer to a plurality of nodesin a hierarchically lower layer; and each of which features a synapticstrength value which has been generated during a back-propagationlearning process using spectral data from carbohydrate materialsanalogous to the carbohydrate material under analysis; and (v) each ofthe nodes featuring a threshold value which has been generated during aback propagation learning process using spectral data from carbohydratematerials analogous to the carbohydrate material under analysis; and (g)applying the digital data corresponding to incremental portions of thespectral information relating to the material under analysis to theinput nodes of the neural network in order to generate at the outputnodes information that is useful to indicate and identify the structureof the material under analysis.
 36. A method according to claim 35 inwhich the step of supplying at least one off-line neural networkcomprises supplying at least one feed forward off-line neural network.37. A method according to claim 35 in which the steps of subjecting thematerial under analysis to energy in a spectroscopy analytical means,sensing transformations in the energy imparted by the material andproducing spectral information corresponding to the energytransformations and the structure of the material are performed using anuclear magnetic resonance spectroscopy device.
 38. A method accordingto claim 35 further comprising the step of deselecting undesiredspectral information that corresponds to impurities in the material. 39.A method according to claim 35 further comprising the step ofnormalizing the spectral information with respect to a predeterminedpoint in the spectrum.
 40. A method according to claim 35 in which thegenerated information indicates the material under analysis.
 41. Amethod according to claim 35 in which the generated informationindicates portions of the material under analysis.
 42. A method foranalyzing and identifying the structure of a particular organic materialby recognizing patterns in free induction decay information that arecharacteristic of such materials, comprising the steps of:(a) subjectingthe organic material under analysis to energy in a nuclear magneticresonance device; (b) sensing transformations in the energy imparted bythe material; (c) producing free induction decay informationcorresponding to the energy transformations and the structure of thematerial; (d) selecting portions of the free induction decay informationwhich are desired for use in identifying the material; (e) digitizing aplurality of incremental portions of the selected free induction decayinformation; (f) supplying an off-line neural network whichcomprises:(i) an input layer comprising a plurality of input nodes, eachof which is capable of receiving digital data corresponding to anincremental portion of the free induction decay information; (ii) anoutput layer hierarchically lower than the input layer comprising aplurality of output nodes, for indicating and identifying the structureof the material under analysis; (iii) a plurality of synapticconnections, each of which connects a node in a hierarchically higherlayer to a plurality of nodes in a hierarchically lower layer; and eachof which features a synaptic strength value which has been generatedduring a back-propagation learning process using spectral data fromorganic materials analogous to the organic material under analysis; and(iv) each of the nodes featuring a threshold value which has beengenerated during a back propagation learning process using spectral datafrom organic materials analogous to the organic material under analysis;and (g) applying the digital data corresponding to incremental portionsof the free induction decay information relating to the material to theinput nodes of the off-line neural network in order to generate at theoutput nodes information that is useful to indicate and identify thestructure of the material under analysis.
 43. A method according toclaim 42 in which the step of supplying at least one off-line neuralnetwork comprises supplying at least one off-line neural network whichincludes at least one hidden layer hierarchically intermediate the inputand output layers comprising a plurality of hidden nodes.
 44. A methodfor analyzing and identifying the structure of a particular organicmaterial by recognizing patterns in spectral information that arecharacteristic of such materials, comprising the steps of:(a) subjectingthe organic material under analysis to energy in at least two analyticalmeans; (b) sensing transformations in the energy imparted by thematerial; (c) producing spectral information corresponding to the energytransformations and the structure of the material; (d) selectingportions of the spectral information which are desired for use inidentifying the material; (e) digitizing a plurality of incrementalportions of the selected spectral information; (f) supplying at leastone off-line neural network which comprises:(i) at least one input layercomprising a plurality of input nodes, each of which is capable ofreceiving digital data corresponding to an incremental portion of thespectral information; (ii) an output layer hierarchically lower than theinput layer comprising a plurality of output nodes, for indicating andidentifying the structure of the material under analysis; and (iii) aplurality of synaptic connections, each of which connects a node in ahierarchically higher layer to a plurality of nodes in a hierarchicallylower layer; and each of which features a synaptic strength value whichhas been generated during a back-propagation learning process usingspectral data from organic materials analogous to the organic materialunder analysis; and (iv) each of the nodes featuring a threshold valuewhich has been generated during a back propagation learning processusing spectral data from organic materials analogous to the organicmaterial under analysis; and (g) applying the digital data correspondingto incremental portions of the spectral information relating to thematerial to the input nodes of the off-line neural network in ordergenerate information at the output nodes that is useful to indicate andidentify the structure of the material under analysis.
 45. A methodaccording to claim 44 in which the step of supplying at least oneoff-line neural network comprises supplying an off-line neural networkthat includes at least one hidden layer hierarchically intermediate aninput and the output layer comprising a plurality of hidden nodes.
 46. Amethod according to claim 44 in which the analytical means comprise gaschromatograph and mass spectrograph means.
 47. A method according toclaim 44 in which the off-line neural network contains two input layersof neurons, one input layer corresponding to mass spectral informationand the other input layer corresponding to gas chromatographinformation.
 48. A method according to claim 45 in which the off-lineneural network contains a single hidden layer, and the neurons in themass spectral information input layer and the neurons in the gaschromatograph information input layer are each connected to each neuronin the hidden layer.
 49. A method according to claim 45 in which theoff-line neural network contains two hidden layers, a first in whicheach neuron is connected to every neuron in the mass spectralinformation input layer and a second in which each neuron is connectedto every neuron in the gas chromatograph information input layer, andthe neurons in each hidden layer are each connected to each neuron inthe output layer.
 50. A method for analyzing and identifying thestructure of a particular organic material by recognizing patterns inspectral information that are characteristic of such materials,comprising the steps of:(a) subjecting the organic material underanalysis to a first and a second type of energy in at least onespectroscopy device; (b) sensing transformations in the energy impartedby the material in the device; (c) producing a first and second set ofspectral information corresponding to the transformations in the firstand second energy types and to the structure of the material; (d)selecting portions of the spectral information which are desired for usein identifying the material; (e) digitizing a plurality of incrementalportions of the selected spectral information; (f) supplying at leastone off-line neural network which comprises:(i) at least one input layercomprising a plurality of input nodes, each of which is capable ofreceiving digital data corresponding to an incremental portion of thespectral information; (ii) an output layer hierarchically lower than theinput layer comprising a plurality of output nodes, for indicating andidentifying the structure of the material under analysis; (iii) aplurality of synaptic connections, each of which connects a node in ahierarchically higher layer to a plurality of nodes in a hierarchicallylower layer; and each of which features a synaptic strength value whichhas been generated during a back-propagation learning process usingspectral data from organic materials analogous to the organic materialunder analysis; and (iv) each of the nodes featuring a threshold valuewhich has been generated during a back propagation learning processusing spectral data from organic materials analogous to the organicmaterial under analysis; and (g) applying the digital data correspondingto incremental portions of the spectral information relating to thematerial to the input nodes of the neural network in order to generateinformation at the output nodes that is useful to indicate and identifythe structure of the material under analysis.
 51. A method according toclaim 50 in which the step of supplying at least one off-line neuralnetwork comprises supplying at least one off-line neural network whichincludes at least one hidden layer hierarchically intermediate an inputand the output layer comprising a plurality of hidden nodes.
 52. Amethod according to claim 50 in which the off-line neural networkcontains two input layers of neurons, one input layer corresponding tothe first information set and the other input layer corresponding to thesecond information set.
 53. A method according to claim 51 in which theoff-line neural network contains a single hidden layer, and the neuronsin the first information set input layer and the neurons in the secondinformation set input layer are each connected to each neuron in thehidden layer.
 54. A method according to claim 51 in which the off-lineneural network contains two hidden layers, a first in which each neuronis connected to every neuron in the first information set input layerand a second in which each neuron is connected to every neuron in thesecond information set input layer, and the neurons in each hidden layerare each connected to each neuron in the output layer.
 55. A methodaccording to claim 50 in which steps (a) through (c) are performed inseparate spectroscopy devices.
 56. A method according to claim 25 inwhich the generated information corresponds to close structuralrelatives to the material under analysis.
 57. A method according toclaim 35 in which the generated information corresponds to closestructural relatives to the material under analysis.